ANTE: Anticipate, Then Execute – Learning Internal World Models for Coding Agents from Verified Beliefs

Yi Ge, Kangrui Ruan, Jifan Chen, Manish Prajapat, Christian Bock, Xiaoyang Wang, Rein Houthooft, Aritra Sengupta, Varun Kumar

arXiv 2026 Preprint.

LLM AgentAgentic RLWorld ModelCoding

PaperWebsiteHuggingFaceVideoX post

An agentic RL framework (code world model) with internal belief.

PatternBloom: Empowering Agentic RAG with Externalized RL-Distilled Graph Patterns

Yi Ge, Heng Huang

NeurIPS 2026 The 40th Annual Conference on Neural Information Processing Systems.

LLM AgentAgentic RLRAGMemory

PaperCode

An agentic RAG framework that distills RL-discovered reasoning patterns into externalized skills.

Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following

Tianyi Xiong (*), Yi Ge (*), Ming Li, Zuolong Zhang, Pranav Kulkarni, Kaishen Wang, Qi He, Zeying Zhu, Chenxi Liu, Ruibo Chen, Tong Zheng, Yanshuo Chen, Xiyao Wang, Renrui Zhang, Wenhu Chen, Heng Huang

CVPR 2026Highlight IEEE/CVF Conference on Computer Vision and Pattern Recognition.

MultimodalEvaluation

PaperCodeWebsiteDataset

Evaluating multimodal judges on their capacity to follow pluralistic criteria and produce reliable criterion-level judgments.

FlexDataset: Crafting Annotated Dataset Generation for Diverse Applications

Yi Ge, Leo Shawn

AAAI 2025Oral Published. 39th AAAI Conference on Artificial Intelligence.

MultimodalAIGCDiffusionVisual Perception

PaperCodeVideoPoster

A framework for generating high-fidelity synthetic and annotated datasets using a composition-to-data (C2D) paradigm.

Reducing Divergence in Batch Normalization for Domain Adaptation

Yi Ge, Mingjing Wu, Zhenhao Chen

AAAI 2025Oral Published. 39th AAAI Conference on Artificial Intelligence.

Domain AdaptationVisual Perception

PaperCode

A normalization paradigm for unsupervised domain adaptation that mitigates estimation shifts accumulated in batch normalization layers.

DRUM: Learning Demonstration Retriever for Large MUlti-modal Models

Yi Ge, Jiechao Gao, Wei Han, Wei Zhu

ACL 2025 Published. The 63rd Annual Meeting of the Association for Computational Linguistics. SRW.

MultimodalIn-Context LearningRAG

Paper

Demonstration retrieval tailored for large vision-language models (LVLMs).

Sharpness-Aware Minimization for Adversarial Domain Adaptation

Zhenhao Chen(*), Yi Ge(*), Wei Wang, Tianyi Liang

TNNLS 2024 Under Revision. IEEE Transactions on Neural Networks and Learning Systems.

Domain Adaptation

PaperCode

A framework for adversarial domain adaptation that integrates Interchangeable Batch Normalization (InterBN) and sharpness-aware optimization.

SphereVLAD++: Attention-based and Signal-enhanced Viewpoint Invariant Descriptor

Shiqi Zhao, Peng Yin, Yi Ge, Sebastian Scherer

RA-L 2022 Published. IEEE Robotics and Automation Letters.

3DRoboticsVisual Perception

PaperBibTexCode

An advanced method for 3D place recognition using LiDAR data.

Conditional Link Prediction of Category-Implicit Keypoint Detection

Yi Ge, Rui Fan, Zechun Liu, Zhiqiang Shen

WACV 2022 Published. 26th Winter Conference on Applications of Computer Vision.

Visual Perception

PaperBibTexTalk

A network for semantic keypoint detection and link prediction in multi-class instances.

Learning 3D Segmentation from Sparse Annotations via Hierarchical Descriptors

Yi Ge, Rui Fan, Zechun Liu, Zhiqiang Shen

ITCA 2023 Published. 2nd International Conference on Information Technology and Computer Application.

3DVisual Perception

PaperDemoMini Demo

A approach that can simultaneously learn segmentation from sparse annotations via reasoning global-regional structures and individual-vicinal properties.

Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training

Xiaofeng Liu, Yuzhuo Han, Yi Ge, Song Bai, Tianxing Wang, Xu Han, Site Li, Jane You, Jun Lu

AAAI 2020Oral Published. 34th AAAI Conference on Artificial Intelligence.

Visual Perception

PaperBibTex

Incorporates the importance-aware inter-class correlation in a Wasserstein training framework by configuring its ground distance matrix.

Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis

Xiaofeng Liu, Xu Han, Yukai Qiao (*), Yi Ge (*), Lu Jun

ICCVW 2019Oral Published. International Conference on Computer Vision Workshops.

Medical

PaperBibTex

Addresses the challenges associated with discrete and successively distributed labels in medical diagnosis, like diabetic retinopathy, by incorporating the inter-class correlations in the loss function.